Data clean room

ABSTRACT

Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

TECHNICAL FIELD

The present disclosure generally relates to securely analyzing dataacross different accounts using a data clean room.

BACKGROUND

Currently, most digital advertising is performed using third-partycookies. Cookies are small pieces of data generated and sent from a webserver and stored on the user's computer by the user's web browser thatare used to gather data about customers' habits based on their websitebrowsing history. Because of privacy concerns, the use of cookies isbeing restricted.

Companies may want to create target groups for advertising or marketingefforts for specific audience segments. To do so, companies may want tocompare their customer information with that of other companies to seeif their customer lists overlap for the creation of such target groups.Thus, companies may want to perform data analysis, such as an overlapanalysis, of their customers or other data. To perform such types ofdata analyses, companies can use “trusted” third parties, who can accessdata from each of the companies and perform the data analysis. However,this third-party approach suffers from significant disadvantages. First,companies give up control of their customer data to these third parties,which can lead to unforeseen and harmful consequences because this datacan contain sensitive information, such as personal identityinformation. Second, the analysis is performed by the third parties, notthe companies themselves. Thus, the companies have to go back to thethird parties to conduct a more detailed analysis or a differentanalysis. This can increase the expense associated with the analysis aswell as add a time delay. Also, providing such information to thirdparties for this purpose may run afoul of ever-evolving data privacyregulations and common industry policies.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which anetwork-based data warehouse system can implement streams on shareddatabase objects, according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 is a block diagram illustrating accounts in a data warehousesystem, according to some example embodiments.

FIG. 5 is a block diagram illustrating a data clean room, according tosome example embodiments.

FIG. 6 is a block diagram illustrating a double-blind data clean room,according to some example embodiments.

FIG. 7 shows a flow diagram for on-boarding data to a data warehousesystem, according to some example embodiments.

FIGS. 8A-8C is a block diagram illustrating a secure query using a dataclean room, according to some example embodiments.

FIG. 9 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Embodiments of the present disclosure may provide a data clean roomallowing secure data analysis across multiple accounts, without the useof third parties. Each account may be associated with a differentcompany or party. The data clean room may provide security functions tosafeguard sensitive information. For example, the data clean room mayrestrict access to data in other accounts. The data clean room may alsorestrict which data may be used in the analysis and may restrict theoutput. For example, the output may be restricted based on a minimumthreshold of overlapping data (e.g., elements per output data row).Therefore, each account (e.g., company, party) may keep control of itsdata in its own account while being able to perform data analysis usingits own data and data from other accounts. Each account may set policiesfor which types of data and which types of analysis it is willing toallow other accounts to perform. The overlap data may be anonymized toprevent sensitive information from being revealed.

FIG. 1 illustrates an example shared data processing platform 100implementing secure messaging between deployments, in accordance withsome embodiments of the present disclosure. To avoid obscuring theinventive subject matter with unnecessary detail, various functionalcomponents that are not germane to conveying an understanding of theinventive subject matter have been omitted from the figures. However, askilled artisan will readily recognize that various additionalfunctional components may be included as part of the shared dataprocessing platform 100 to facilitate additional functionality that isnot specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based data warehouse system 102, a cloud computing storageplatform 104 (e.g., a storage platform, an AWS® service, MicrosoftAzure®, or Google Cloud Services®), and a remote computing device 106.The network-based data warehouse system 102 is a network-based systemused for storing and accessing data (e.g., internally storing data,accessing external remotely located data) in an integrated manner, andreporting and analysis of the integrated data from the one or moredisparate sources (e.g., the cloud computing storage platform 104). Thecloud computing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based data warehouse system102. While in the embodiment illustrated in FIG. 1, a data warehouse isdepicted, other embodiments may include other types of databases orother data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based data warehouse system 102.The remote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views, as discussed in further detail below.

The network-based data warehouse system 102 comprises an accessmanagement system 110, a compute service manager 112, an executionplatform 114, and a database 116. The access management system 110enables administrative users to manage access to resources and servicesprovided by the network-based data warehouse system 102. Administrativeusers can create and manage users, roles, and groups, and usepermissions to allow or deny access to resources and services. Theaccess management system 110 can store share data that securely managesshared access to the storage resources of the cloud computing storageplatform 104 amongst different users of the network-based data warehousesystem 102, as discussed in further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based data warehouse system 102. The compute service manager112 also performs query optimization and compilation as well as managingdusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 dusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based datawarehouse system 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-n that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-n are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-n may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-n may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based data warehouse system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based data warehousesystem 102 and the cloud computing storage platform 104. The web proxy120 handles tasks involved in accepting and processing concurrent APIcalls, including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1, data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based data warehouse system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based data warehouse system 102 toscale quickly in response to changing demands on the systems andcomponents within network-based data warehouse system 102. Thedecoupling of the computing resources from the data storage devices124-1 to 124-n supports the storage of large amounts of data withoutrequiring a corresponding large amount of computing resources.Similarly, this decoupling of resources supports a significant increasein the computing resources utilized at a particular time withoutrequiring a corresponding increase in the available data storageresources. Additionally, the decoupling of resources enables differentaccounts to handle creating additional compute resources to process datashared by other users without affecting the other users' systems. Forinstance, a data provider may have three compute resources and sharedata with a data consumer, and the data consumer may generate newcompute resources to execute queries against the shared data, where thenew compute resources are managed by the data consumer and do not affector interact with the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based data warehousesystem 102 is dynamic and supports regular changes to meet the currentdata processing needs.

During typical operation, the network-based data warehouse system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1, the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-n in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-n. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, a request processing service 202 managesreceived data storage requests and data retrieval requests (e.g., jobsto be performed on database data). For example, the request processingservice 202 may determine the data necessary to process a received query(e.g., a data storage request or data retrieval request). The data maybe stored in a cache within the execution platform 114 or in a datastorage device in cloud computing storage platform 104. A managementconsole service 204 supports access to various systems and processes byadministrators and other system managers. Additionally, the managementconsole service 204 may receive a request to execute a job and monitorthe workload on the system. The stream share engine 225 manages changetracking on database objects, such as a data share (e.g., shared table)or shared view, according to some example embodiments, and as discussedin further detail below.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, a operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based data warehouse system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based data warehouse system 102. For example, data storagedevice 220 may represent caches in execution platform 114, storagedevices in cloud computing storage platform 104, or any other storagedevice.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, execution platform 114 includes multiplevirtual warehouses, which are elastic clusters of compute instances,such as virtual machines. In the example illustrated, the virtualwarehouses include virtual warehouse 1, virtual warehouse 2, and virtualwarehouse n. Each virtual warehouse (e.g., EC2 duster) includes multipleexecution nodes (e.g., virtual machines) that each include a data cacheand a processor. The virtual warehouses can execute multiple tasks inparallel by using the multiple execution nodes. As discussed herein,execution platform 114 can add new virtual warehouses and drop existingvirtual warehouses in real time based on the current processing needs ofthe systems and users. This flexibility allows the execution platform114 to quickly deploy large amounts of computing resources when neededwithout being forced to continue paying for those computing resourceswhen they are no longer needed. All virtual warehouses can access datafrom any data storage device (e.g., any storage device in cloudcomputing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-n shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 124-1 to124-n and, instead, can access data from any of the data storage devices124-1 to 124-n within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-n. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-n. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-n includes a cache 304-n and aprocessor 306-n. Each execution node 302-1, 302-2, and 302-n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-n. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-nincludes a cache 314-n and a processor 316-n. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-n.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-n includes a cache 324-n and a processor 326-n.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

FIG. 4 shows an example of two separate accounts in a data warehousesystem, according to some example embodiments. Here, Company A mayoperate an account A 402 with a network-based data warehouse system asdescribed herein. In account A 402, Company A data 404 may be stored.The Company A data 404 may include, for example, customer data 406relating to customers of Company A. The customer data 406 may be storedin a table or other format storing customer information and otherrelated information. The other related information may includeidentifying information, such as email, and other known characteristicsof the customers, such as gender, geographic location, purchasinghabits, and the like. For example, if Company A is a consumer-goodscompany, purchasing characteristics may be stored, such as whether thecustomer is single, married, part of a suburban or urban family, etc. IfCompany A is a streaming service company, information about the watchinghabits of customers may be stored, such as whether the customer likessci-fi, nature, reality, action, etc.

Likewise, Company B may operate an account B 412 with the network-baseddata warehouse system as described herein. In account B 412, Company Bdata 414 may be stored. The Company B data 414 may include, for example,customer data relating customers of Company B. The customer data 416 maybe stored in a table or other format storing customer information andother related information. The other related information may includeidentifying information, such as email, and other known characteristicsof the customers, such as gender, geographic location, purchasinghabits, etc., as described above.

For security reasons, Company A's data may not be accessible to CompanyB and vice versa. However, Company A and Company B may want to share atleast some of their data with each other without revealing sensitiveinformation, such as a customer's personal identity information. Forexample, Company A and Company B may want to explore cross marketing oradvertising opportunities and may want to see how many of theircustomers overlap and filter based on certain characteristics of theoverlapping customers to identify relationships and patterns.

To this end, a data clean room may be provided by the network-based datawarehouse system as described herein. FIG. 5 is a block diagramillustrating a method for operating a data clean room, according to someexample embodiments. The data clean room may enable companies A and B toperform overlap analysis on their company data, without sharingsensitive data and without losing control over the data. The data cleanroom may create linkages between the data for each account and mayinclude a set of blind cross reference tables.

Next, example operations to create the data clean room are described.Account B may include customer data 502 for Company B, and account A mayinclude customer data 504 for Company A. In this example, account B mayinitiate the creation of the data clean room; however, either accountmay initiate creation of the data clean room. Account B may create asecure function 506. The secure function 506 may look up specificidentifier information in account B's customer data 502. The securefunction 506 may anonymize the information by creating identifiers foreach customer data (e.g., generating a first result set). The securefunction 506 may be a secure user-defined function (UDF) and may beimplemented using the techniques described in U.S. patent applicationSer. No. 16/814,875, entitled “System and Method for Global DataSharing,” filed on Mar. 10, 2020, which is incorporated herein byreference in its entirety, including but not limited to those portionsthat specifically appear hereinafter, the incorporation by referencebeing made with the following exception: In the event that any portionof the above-referenced application is inconsistent with thisapplication, this application supersedes the above-referencedapplication.

The secure function 506 may be implemented as a SQL UDF. The securefunction 506 may be defined to protect the underlying data used toprocess the function. As such, the secure function 506 may restrictdirect and indirect exposure of the underlying data.

The secure function 506 may then be shared with account A using a secureshare 508. The secure share 508 may allow account A to execute thesecure function 506 while restricting account A from having access tothe underlying data of account B used by the function and from beingable to see the code of the function. The secure share 508 may alsorestrict account A from accessing the code of the secure function 506.Moreover, the secure share 508 may restrict account A from seeing anylogs or other information about account B's use of the secure function506 or the parameters provided by account B of the secure function 506when it is called.

Account A may execute the secure function 506 using its customer data504 (e.g., generating a second result set). The result of the executionof the secure function 506 may be communicated to account B. Forinstance, a cross reference table 510 may be created in account B, whichmay include anonymized customer information 512 (e.g., anonymizedidentification information). Likewise, a cross reference table 514 maybe created in account A, which may include anonymized customerinformation 516 for matching overlapping customers for both companies,and dummy identifiers for non-matching records. The data from the twocompanies may be securely joined so that neither account may access theunderlying data or other identifiable information. For example, the datamay be securely joined using the techniques described in U.S. patentapplication Ser. No. 16/368,339, entitled “Secure Data Joins in aMultiple Tenant Database System,” filed on May 28, 2019, which isincorporated herein by reference in its entirety, including but notlimited to those portions that specifically appear hereinafter, theincorporation by reference being made with the following exception: Inthe event that any portion of the above-referenced application isinconsistent with this application, this application supersedes theabove-referenced application.

For instance, cross reference table 510 (and anonymized customerinformation 512) may include fields: “my_cust_id,” which may correspondto the customer ID in account B's data; “my_link_id,” which maycorrespond to an anonymized link to the identified customer information;and a “their_link_id,” which may correspond to an anonymized matchedcustomer in company A. “their_link_id” may be anonymized, so thatcompany B cannot discern the identity of the matched customers. Theanonymization may be performed using hashing, encryption, tokenization,or other suitable techniques.

Moreover, to further anonymize the identity, all listed customers ofcompany B in cross reference table 510 (and anonymized customerinformation 512) may have a unique matched customer from company Blisted, irrespective of whether there was an actual match or not. Adummy “their_link_id” may be created for customers not matched. This wayneither company may be able to ascertain identity information of thematched customers. Neither company may discern where there is an actualmatch rather than a dummy returned identifier (no match). Hence, thecross reference tables 510 may include anonymized key-value pairs. Asummary report may be created notifying the total number of matches, butother details of the matched customers may not be provided to safeguardthe identities of the customers.

The data clean room may operate in one or both directions, meaning thata double-blind clean room may be provided. FIG. 6 illustrates is a blockdiagram illustrating a method for operating a double-blind clean room,according to some example embodiments. The double-blind clean room mayenable company A to perform overlap analysis using its company data withthe company data of Company B and vice versa, without sharing sensitivedata and without losing control over their own data. The double-blindclean room may create linkages between the data for each account and mayinclude a set of double-blind cross reference tables.

Here, account A may include its customer data 602, and account B mayinclude its customer data 604. Account A may create a secure function606 (“Get_Link_ID”), as described above. The secure function 606 may beshared with account B using a secure share 608, as described above.Moreover, a stored-procedures function 610 may detect changes to data inrespective customer data and may update and refresh links accordingly.

The same or similar process may be applied from account B to account Awith secure function 612, secure share 614, and stored procedures 616.Consequently, the cross reference table 618 in account A may includeinformation about the customer overlap between the two companies. Forexample, the cross reference table 618 includes fields: “my_cust_id,”which may correspond to the customer ID in account A's data;“my_link_id,” which may correspond to an anonymized link to theidentified customer information of company A; and a “their_link_id,”which may correspond to an anonymized matched customer in company B. Theanonymization may be performed using hashing, encryption, tokenization,or the like.

Similarly, the cross reference table 620 in account B may includeinformation about the customer overlap between the two companies. Forexample, the cross reference table 620 includes fields: “my_cust_id,”which may correspond to the customer ID in account B's data;“my_link_id,” which may correspond to an anonymized link to theidentified customer information of company B; and a “their_link_id,”which may correspond to an anonymized matched customer in company A. Theanonymization may be performed using hashing, encryption, tokenization,or other suitable techniques.

In the above examples both company A and B had accounts with the datawarehouse system. However, the blind clean room techniques describedherein may also find applications when one or both companies do not haveaccounts with the data warehouse system. FIG. 7 illustrates a techniquefor on-boarding data to a data warehouse system, according to someexample embodiments. Here, Company C may not have an account with thedata warehouse system but may still nonetheless want to employ the dataclean room techniques described herein. Company C may load its companydata into a load file 702 (e.g., a .csv file). Then, using a browser 704or an app or the like, Company C may use a file uploader 706 upload theload file 702 to a secure cloud storage location 708 (also referred toas “enclave bucket”). From the enclave bucket 708, the data may be movedinto an enclave account 710. The data may be moved by a batch dataingestion command, a copy command, or the like. A custom web GUI 712 mayalso be used to send control information to the enclave account 710. Thecontrol information, for example, may set access restrictions for thedata in the load file 702. For the purpose of creating and using a dataclean room, the enclave account 710 may then function and operate as aregular account, as described above.

After a data clean room is created, the parties may run secure querieson the secure data to gather more detailed information. In the exampleof securely matching customers of two companies, either company may senda query request to the other company to determine the count of matchesbased on select conditions.

FIGS. 8A-8C is a block diagram illustrating a method for processing asecure query using a data clean room, according to some exampleembodiments. The example in FIGS. 8A-8C builds on the example data cleanroom created in FIG. 6. Here, account A may include its customer data602, and account B may include its customer data 604. As explainedabove, cross reference tables 618, 620 may be created and included inaccounts A and B, respectively. In addition, sales data 802 (shown inFIG. 8C), which includes information related to customers' buyinghabits, may be included in account A, and watch data 804, which mayinclude information about customers' viewing habits, may be included inaccount B.

Moreover, account B may use a secure view 806 to allow company A to haveaccess to select data, referred to as secure query usable data 808.Account B may notify account A of this secure query usable data 808 in avariety of ways. Account B may publish this information to account A inadvance. It may share the structure and lookup keys of the secure queryusable data 808 with account A. It may also use a private data exchangeusing the techniques described in U.S. patent application Ser. No.16/746,673, entitled “Private Data Exchange,” filed on Jan. 17, 2020,which is incorporated herein by reference in its entirety, including butnot limited to those portions that specifically appear hereinafter, theincorporation by reference being made with the following exception: Inthe event that any portion of the above-referenced application isinconsistent with this application, this application supersedes theabove-referenced application.

A user in account A may run a secure query 810 in the data clean room.The query may request processing of data in both accounts A and B, butmay restrict the user from having access to sensitive data of account B.For example, the user may run a query requesting information: How manyof my (Company A's) customers have watched one of Company B's programs,grouped by Company B programs and my (Company A's) segments also boughtmy “Top Paper Towels” product, who I know live in the US or Canada, andwho are also animation fans according to Company B, where there are atleast two customers in each resulting group?

Using Company A's customer data 602, cross reference table 618, andsales data 802, the query may generate an interim table 812 (“MyData1”).The interim table 812, in this example, may include anonymized CompanyA's customer information for customers who bought “Top Paper Towels” andwho live in the US or Canada securely joined with matching anonymizedCompany B customers (which may or may not include “dummy” matchedaccounts as described above). A secure query request 814 may also begenerated to send to Company B. The secure query request 814 may requestCompany B to run the remaining portion of the query and to send back thefinal results. For example, the secure query request 814 may include arequest ID, filters for Company B to apply (“select_c” and “where_c”),and an output format for the final results (“result_table”). The securequery request 814 may be provided in the form of a request table (asshown) or may be another type of remote procedure call, such as a SQLstatement. The interim table 812 and secure query request 814 may beshared with account B using secure query request share 816.

Next, account B may complete the remaining portion of the originalquery. At account B, a copy of the interim table 818 may be stored. Thesecure query request 814 may be received by a stream on request 820 andtask on new request 822 functions. The secure query may then be executedby a restricted query procedure function 824. This function may use datafrom a variety of sources, such as the copy of the interim table 818,the cross reference table 620, the watch data 804, and the secure queryusable data 808, to execute the query. In this example, the restrictedquery procedure function 824 may filter customers identified in theinterim table 818 by those who have watched one of Company B's programand are animation fans, and the function may group the results by theprogram and Company A's segments, as shown in a query status 826. Theresults may be generated and output to an interim result table 828.Next, the last part of the query may be performed: filter out resultswith fewer than two customers in each resulting group (e.g., a minimumthreshold). In the example shown in FIGS. 8A-8C, the group for “Movie B”and a particular Company A segment had only one result, so it wasremoved. The final results may then be shared with account A using asecure query result share function 830. The results in account A may beprovided as a final result table 832 (“res1”) showing the matches of thequery grouped by the two programs.

FIG. 9 illustrates a diagrammatic representation of a machine 900 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 900 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions916 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 916 may cause the machine 900 to execute any one ormore operations of any one or more of the methods described herein. Asanother example, the instructions 916 may cause the machine 900 toimplemented portions of the data flows described herein. In this way,the instructions 916 transform a general, non-programmed machine into aparticular machine 900 (e.g., the remote computing device 106, theaccess management system 110, the compute service manager 112, theexecution platform 114, the access management system 118, the Web proxy120, remote computing device 106) that is specially configured to carryout any one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 900 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 900 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 916, sequentially orotherwise, that specify actions to be taken by the machine 900. Further,while only a single machine 900 is illustrated, the term “machine” shallalso be taken to include a collection of machines 900 that individuallyor jointly execute the instructions 916 to perform any one or more ofthe methodologies discussed herein.

The machine 900 includes processors 910, memory 930, and input/output(I/O) components 950 configured to communicate with each other such asvia a bus 902. In an example embodiment, the processors 910 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 912 and aprocessor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors 910 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 916 contemporaneously. AlthoughFIG. 9 shows multiple processors 910, the machine 900 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 930 may include a main memory 932, a static memory 934, and astorage unit 936, all accessible to the processors 910 such as via thebus 902. The main memory 932, the static memory 934, and the storageunit 936 store the instructions 916 embodying any one or more of themethodologies or functions described herein. The instructions 916 mayalso reside, completely or partially, within the main memory 932, withinthe static memory 934, within the storage unit 936, within at least oneof the processors 910 (e.g., within the processor's cache memory), orany suitable combination thereof, during execution thereof by themachine 900.

The I/O components 950 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 950 thatare included in a particular machine 900 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 950 mayinclude many other components that are not shown in FIG. 9. The I/Ocomponents 950 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 950 mayinclude output components 952 and input components 954. The outputcomponents 952 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 954 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 or devices 970 via a coupling982 and a coupling 972, respectively. For example, the communicationcomponents 964 may include a network interface component or anothersuitable device to interface with the network 980. In further examples,the communication components 964 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 970 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 900 may correspond to any one of the remote computing device106, the access management system 110, the compute service manager 112,the execution platform 114, the access management system 118, the Webproxy 120, and the devices 970 may include any other of these systemsand devices.

The various memories (e.g., 930, 932, 934, and/or memory of theprocessor(s) 910 and/or the storage unit 936) may store one or more setsof instructions 916 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 916, when executed by the processor(s) 910,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 980may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 980 or a portion of the network980 may include a wireless or cellular network, and the coupling 982 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 982 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 916 may be transmitted or received over the network 980using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916 may be transmitted or received using a transmission medium via thecoupling 972 (e.g., a peer-to-peer coupling) to the devices 970. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 916 for execution by the machine 900, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

The following numbered examples are embodiments:

Example 1. A method comprising: providing a first party data in a firstaccount; providing a second party data in a second account; executing,by a processor, a secure function using the first party data to generatea first result, including creating links to the first party data andanonymizing identification information in the first party data; sharingthe secure function with the second account; executing the securefunction using the second party data to generate a second result andrestricting the second account from accessing the first party data; andgenerating a cross reference table with the first and second results,the cross reference table providing anonymized matches of the first andsecond results.

Example 2. The method of example 1, further comprising: restricting thesecond account from accessing a code of the secure function.

Example 3. The method of any of examples 1-2, further comprising:restricting the second account from logs related to execution of thefirst portion of the secure function.

Example 4. The method of any of examples 1-3, further comprising:generating dummy matching information in the second result for aninstance of no match.

Example 5. The method of any of examples 1-4, further comprising:generating a summary report of the anonymized matches.

Example 6. The method of any of examples 1-5, further comprising:restricting access to the number of matches when the number of matchesis below a minimum threshold.

Example 7. The method of any of examples 1-6, wherein providing thefirst party data includes: uploading a load file to a secure cloudstorage location; storing data from the load file into an enclaveaccount; and setting access restrictions for the data from the load filebased on control information.

Example 8. The method of any of examples 1-7, further comprising:receiving a query request; based at least on the first party data andthe cross reference table, executing a first portion of the queryrequest; generating an interim table based on executing the firstportion of the query request; generating a secure query request,including instructions related to executing a second portion of thequery request; and sharing the secure query request and the interimtable with the second account.

Example 9. The method of any of examples 1-8, further comprising: at thesecond account, executing the secure query request and joining resultsof the secure query requests with information from the interim table togenerate final results of the query request.

Example 10. A system comprising: one or more processors of a machine;and a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations implementing any oneof example methods 1 to 9.

Example 11. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 9.

What is claimed is:
 1. A method comprising: providing a first party datain a first account in a network-based data system; providing a secondparty data in a second account in the network-based data system;executing, by a processor, a secure function using the first party datato generate a first result, including creating links to the first partydata and anonymizing identification information in the first party data;sharing the secure function with the second account; executing thesecure function using the second party data to generate a second resultand restricting the second account from accessing the first party data;generating dummy matching information in the second result for aninstance of no match; and generating a cross reference table with thefirst and second results, the cross reference table providing anonymizedmatches of the first and second results, the cross reference table beingaccessible via the network-based data system for performing analysis ofoverlapping first party and second pay data.
 2. The method of claim 1,further comprising: restricting the second account from accessing a codeof the secure function.
 3. The method of claim 2, further comprising:restricting the second account from logs related to execution of a firstportion of the secure function.
 4. The method of claim 1, furthercomprising: generating a summary report of the anonymized matches. 5.The method of claim 4, further comprising: restricting access to thenumber of anonymized matches when the number of anonymized matches isbelow a minimum threshold.
 6. The method of claim 1, wherein providingthe first party data includes: uploading a load file to a secure cloudstorage location; storing data from the load file into an enclaveaccount; and setting access restrictions for the data from the load filebased on control information.
 7. The method of claim 1, furthercomprising: receiving a query request; based at least on the first partydata and the cross reference table, executing a first portion of thequery request; generating an interim table based on executing the firstportion of the query request; generating a secure query request,including instructions related to executing a second portion of thequery request; and sharing the secure query request and the interimtable with the second account.
 8. The method of claim 7, furthercomprising: at the second account, executing the secure query requestand joining results of the secure query requests with information fromthe interim table to generate final results of the query request.
 9. Amachine-storage medium embodying instructions that, when executed by amachine, cause the machine to perform operations comprising: providing afirst party data in a first account in a network-based data system;providing a second party data in a second account in the network-baseddata system; executing a secure function using the first party data togenerate a first result, including creating links to the first partydata and anonymizing identification information in the first party data;sharing the secure function with the second account; executing thesecure function using the second party data to generate a second resultand restricting the second account from accessing the first party data;generating dummy matching information in the second result for aninstance of no match; and generating a cross reference table with thefirst and second results, the cross reference table providing anonymizedmatches of the first and second results the cross reference table beingaccessible via the network-based data system for performing analysis ofoverlapping first party and second party data.
 10. The machine-storagemedium of claim 9, further comprising: restricting the second accountfrom accessing a code of the secure function.
 11. The machine-storagemedium of claim 10, further comprising: restricting the second accountfrom logs related to execution of a first portion of the securefunction.
 12. The machine-storage medium of claim 9, further comprising:generating a summary report of the anonymized matches.
 13. Themachine-storage medium of claim 12, further comprising: restrictingaccess to the number of anonymized matches when the number of anonymizedmatches is below a minimum threshold.
 14. The machine-storage medium ofclaim 9, wherein providing the first party data includes: uploading aload file to a secure cloud storage location; storing data from the loadfile into an enclave account; and setting access restrictions for thedata from the load file based on control information.
 15. Themachine-storage medium of claim 9, further comprising: receiving a queryrequest; based at least on the first party data and the cross referencetable, executing a first portion of the query request; generating aninterim table based on executing the first portion of the query request;generating a secure query request, including instructions related toexecuting a second portion of the query request; and sharing the securequery request and the interim table with the second account.
 16. Themachine-storage medium of claim 15, further comprising: at the secondaccount, executing the secure query request and joining results of thesecure query requests with information from the interim table togenerate final results of the query request.
 17. A system comprising:one or more processors of a machine; and a memory storing instructionsthat, when executed by the one or more processors, cause the machine toperform operations comprising: providing a first party data in a firstaccount in a network-based data system; providing a second party data ina second account in the network-based data system; executing a securefunction using the first party data to generate a first result,including creating links to the first party data and anonymizingidentification information in the first party data; sharing the securefunction with the second account; executing the secure function usingthe second party data to generate a second result and restricting thesecond account from accessing the first party data; generating dummymatching information in the second result for an instance of no match;and generating a cross reference table with the first and secondresults, the cross reference table providing anonymized matches of thefirst and second results, the cross reference table being accessible viathe network-based data system for performing analysis of overlappingfirst party and second party data.
 18. The system of claim 17, theoperations further comprising: restricting the second account fromaccessing a code of the secure function.
 19. The system of claim 18, theoperations further comprising: restricting the second account from logsrelated to execution of a first portion of the secure function.
 20. Thesystem of claim 17, the operations further comprising: generating asummary report of the anonymized matches.
 21. The system of claim 20,the operations further comprising: restricting access to the number ofanonymized matches when the number of anonymized matches is below aminimum threshold.
 22. The system of claim 17, wherein providing thefirst party data includes: uploading a load file to a secure cloudstorage location; storing data from the load file into an enclaveaccount; and setting access restrictions for the data from the load filebased on control information.
 23. The system of claim 17, furthercomprising: receiving a query request; based at least on the first partydata and the cross reference table, executing a first portion of thequery request; generating an interim table based on executing the firstportion of the query request; generating a secure query request,including instructions related to executing a second portion of thequery request; sharing the secure query request and the interim tablewith the second account.
 24. The system of claim 23, further comprising:at the second account, executing the secure query request and joiningresults of the secure query requests with information from the interimtable to generate final results of the query request.